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+Copyright 2023- Preferred Networks, Inc. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+   Licensed under the Apache License, Version 2.0 (the "License");
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+---
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+This software contains modified codes from huggingface trainsformers library which is released under Apache v2.0 license.
+
+---
+Copyright 2018- The Hugging Face team. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
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+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
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+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
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+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
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+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -0,0 +1,89 @@
+# PLaMo-13B
+
+## Model Description
+PLaMo-13B is a Llama 13B model pre-trained on English and Japanese open datasets, developed by Preferred Networks, Inc.
+PLaMo-13B is released under Apache v2.0 license.
+
+PLaMo-13BはPreferred Networks, Incが英語と日本語の公開データセットで訓練したLlama-13Bモデルです。
+PLaMo-13BはApache v2.0ライセンスでリリースされています。
+
+## Usage
+
+### Use a pipeline as a high-level helper
+```
+import transformers
+pipeline = transformers.pipeline("text-generation", model="pfnet/plamo-13b", trust_remote_code=True)
+print(pipeline("The future of artificial intelligence technology is ", max_new_tokens=32))
+```
+
+### Load model directly
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("pfnet/plamo-13b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("pfnet/plamo-13b", trust_remote_code=True)
+text = "これからの人工知能技術は"
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+generated_tokens = model.generate(
+    inputs=input_ids,
+    max_new_tokens=32,
+    do_sample=True,
+    top_k=50,
+    top_p=0.95,
+    temperature=1.0,
+)[0]
+print(tokenizer.decode(generated_tokens))
+
+```
+
+## Model Details
+
+- Model size: 13B
+- Trained tokens: 1.5T tokens (English: 1.32T tokens, Japanese: 0.18T tokens)
+- Developed by: Preferred Networkfs, Inc
+- Model type: Causal decoder-only
+- Language(s): English, Japanese
+- License: Apache v2.0
+
+## Training Dataset
+
+### English
+
+- C4 - English
+- Project Gutenberg
+- RedPajama - Arxiv
+- RedPajama - CommonCrawl - English
+- RedPajama - Github
+- RedPajama - StackExchange
+- RedPajama - Wikipedia
+
+### Japanese
+
+- mC4 - Japanese
+- Wikipedia - Japanese
+
+## Tokenizer
+PLaMo-13B uses sentencepiece tokenizer which is trained on a subset of the datasets for model pre-training.
+
+## Bias, Risks, and Limitations
+PLaMo-13B is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-13B’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-13B, developers should perform safety testing and tuning tailored to their specific applications of the model.
+
+## How to cite
+```
+@online{PLaMo2023Introducing,
+    author    = {Preferred Networks, Inc},
+    title     = {PLaMo-13B},
+    year      = {2023},
+    url       = {https://huggingface.co/pfnet/plamo-13b},
+    urldate   = {2023-09-28}
+}
+```
+
+## Citations
+```
+@article{touvron2023llama,
+  title={LLaMA: Open and Efficient Foundation Language Models},
+  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
+  journal={arXiv preprint arXiv:2302.13971},
+  year={2023}
+}
+```

config.json

@@ -0,0 +1,29 @@
+{
+    "architectures": [
+        "PlamoForCausalLM"
+    ],
+    "auto_map": {
+        "AutoConfig": "modeling_plamo.PlamoConfig",
+        "AutoModelForCausalLM": "modeling_plamo.PlamoForCausalLM"
+    },
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 16640,
+    "max_position_embeddings": 8192,
+    "model_type": "plamo",
+    "n_shared_head": 8,
+    "num_attention_heads": 40,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 40,
+    "pad_token_id": 0,
+    "rms_norm_eps": 1e-06,
+    "tie_word_embeddings": false,
+    "tokenizer_class": "PlamoTokenizer",
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.32.0",
+    "use_cache": false,
+    "vocab_size": 50432
+}

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.32.0",
+  "use_cache": false
+}

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model.safetensors.index.json

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+}

modeling_plamo.py

@@ -0,0 +1,705 @@
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+
+
+class DecoderInput(NamedTuple):
+    hidden_states: torch.Tensor
+    position_ids: torch.Tensor
+    attention_mask: Optional[torch.Tensor] = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    output_hidden_states: Optional[bool] = False
+    output_attentions: Optional[bool] = False
+    use_cache: Optional[bool] = False
+    gradient_checkpointing: bool = False
+
+
+class DecoderOutput(NamedTuple):
+    hidden_states: torch.Tensor
+    all_hidden_states: Optional[Tuple[torch.Tensor, ...]]
+    all_self_attns: Optional[Tuple[torch.Tensor, ...]]
+    next_decoder_cache: Optional[Tuple[torch.Tensor, ...]]
+
+
+class PlamoConfig(PretrainedConfig):  # type: ignore
+    model_type: str = "plamo"
+
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        hidden_size: int = 4096,
+        intermediate_size: int = 13312,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 32,
+        num_key_value_heads: Optional[int] = None,
+        max_position_embeddings: int = 2048,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        tokenizer_class: str = "PlamoTokenizer",
+        pad_token_id: Optional[int] = None,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        n_shared_head: int = 8,
+        tie_word_embeddings: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+
+        self.n_shared_head = n_shared_head
+
+        super().__init__(
+            tokenizer_class=tokenizer_class,
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: Tuple[int, int], dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+) -> torch.Tensor:
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None) -> torch.Tensor:
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)  # type: ignore
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(
+        self, dim: int, max_position_embeddings: int = 2048, base: int = 10000, device: Optional[torch.device] = None
+    ) -> None:
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len: int, device: Any, dtype: Any) -> None:
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)  # type: ignore
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),  # type: ignore
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),  # type: ignore
+        )
+
+
+def _rotate_half(x: torch.Tensor) -> torch.Tensor:
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def _rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor:
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    x_embed = (x * cos) + (_rotate_half(x) * sin)
+    return x_embed
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class Attention(torch.nn.Module):
+    def __init__(self, config: PlamoConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        head_dim = self.hidden_size // config.num_attention_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        self.q_num_heads = config.num_attention_heads
+        self.qk_dim = self.v_dim = head_dim
+        self.k_num_heads = self.v_num_heads = int(np.ceil(self.q_num_heads / config.n_shared_head))
+
+        self.q_proj = nn.Linear(self.hidden_size, self.q_num_heads * self.qk_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.k_num_heads * self.qk_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.v_num_heads * self.v_dim, bias=False)
+        self.o_proj = nn.Linear(self.q_num_heads * self.v_dim, self.hidden_size, bias=False)
+        self.rotary_emb = RotaryEmbedding(self.qk_dim, max_position_embeddings=self.max_position_embeddings)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.v_num_heads, self.v_dim).transpose(1, 2)
+
+        def _expand_kv(t: torch.Tensor, repeat: int, target: int) -> torch.Tensor:
+            return t.repeat(1, repeat, 1, 1)[:, :target]
+
+        # expand shared kv
+        assert self.k_num_heads == self.v_num_heads
+        key_states = _expand_kv(key_states, self.config.n_shared_head, self.q_num_heads)
+        value_states = _expand_kv(value_states, self.config.n_shared_head, self.q_num_heads)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        assert position_ids is not None
+        query_states = _rotary_pos_emb(query_states, cos, sin, position_ids)
+        key_states = _rotary_pos_emb(key_states, cos, sin, position_ids)
+        # [bsz, nh, t, hd]
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask)
+        attn_output = attn_output.transpose(1, 2)
+
+        attn_output = attn_output.reshape(bsz, q_len, self.q_num_heads * self.v_dim)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MLP(nn.Module):
+    def __init__(self, config: PlamoConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = torch.nn.functional.silu
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))  # type: ignore
+
+
+class PlamoDecoderLayer(torch.nn.Module):
+    def __init__(self, config: PlamoConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.self_attn = Attention(config)
+        self.mlp = MLP(config)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[Any, ...]:
+        # from LlamaDecoder
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+
+        # Self Attention
+        hidden_states_sa, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+        # Fully Connected
+        hidden_states_mlp = self.mlp(hidden_states)
+
+        # Residual
+        hidden_states = residual + hidden_states_sa + hidden_states_mlp
+
+        outputs: Any = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs  # type: ignore
+
+
+class PlamoDecoder(torch.nn.Module):
+    def __init__(self, config: PlamoConfig) -> None:
+        super().__init__()
+        self.layers = torch.nn.ModuleList([PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(self, x: DecoderInput) -> DecoderOutput:
+        all_hidden_states: Optional[Tuple[torch.Tensor, ...]] = () if x.output_hidden_states else None
+        all_self_attns: Optional[Tuple[torch.Tensor, ...]] = () if x.output_attentions else None
+        next_decoder_cache: Optional[Tuple[torch.Tensor, ...]] = () if x.use_cache else None
+        hidden_states = x.hidden_states
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if x.output_hidden_states:
+                assert all_hidden_states is not None
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = x.past_key_values[idx] if x.past_key_values is not None else None
+
+            if self.training and x.gradient_checkpointing:
+
+                def create_custom_forward(module):  # type: ignore
+                    def custom_forward(*inputs):  # type: ignore
+                        # None for past_key_value
+                        return module(*inputs, x.output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),  # type: ignore
+                    hidden_states,
+                    x.attention_mask,
+                    x.position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=x.attention_mask,
+                    position_ids=x.position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=x.output_attentions,
+                    use_cache=x.use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if x.use_cache:
+                cache = layer_outputs[2 if x.output_attentions else 1]
+                assert cache is not None
+                assert next_decoder_cache is not None
+                next_decoder_cache += (cache,)
+
+            if x.output_attentions:
+                assert layer_outputs[1] is not None
+                assert all_self_attns is not None
+                all_self_attns += (layer_outputs[1],)
+        return DecoderOutput(hidden_states, all_hidden_states, all_self_attns, next_decoder_cache)
+
+
+class PlamoPreTrainedModel(PreTrainedModel):  # type: ignore
+    config_class = PlamoConfig
+    _no_split_modules: List[str]
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PlamoDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module: torch.nn.Module) -> None:
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module: torch.nn.Module, value: bool = False) -> None:
+        module.gradient_checkpointing = value  # type: ignore
+
+
+class PlamoModel(PlamoPreTrainedModel):
+    def __init__(self, config: PlamoConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = PlamoDecoder(config)  # type: ignore
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> torch.nn.Embedding:
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_shape: Tuple[int, int],
+        inputs_embeds: Optional[torch.FloatTensor],
+        past_key_values_length: int,
+    ) -> Optional[torch.Tensor]:
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask: Optional[torch.Tensor] = None
+        if input_shape[-1] > 1:
+            assert inputs_embeds is not None
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            assert inputs_embeds is not None
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        assert input_ids is not None
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                use_cache = False
+
+        # decoder layers
+        out = self.layers(
+            DecoderInput(
+                hidden_states,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                output_hidden_states,
+                output_attentions,
+                use_cache,
+                self.gradient_checkpointing,
+            )
+        )
+        assert isinstance(out, DecoderOutput)
+        hidden_states = out.hidden_states
+        all_hidden_states = out.all_hidden_states
+        all_self_attns = out.all_self_attns
+        next_decoder_cache = out.next_decoder_cache
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            assert all_hidden_states is not None
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class PlamoForCausalLM(PlamoPreTrainedModel):
+    def __init__(self, config: PretrainedConfig) -> None:
+        super().__init__(config)
+        self.model = PlamoModel(config)
+
+        self.lm_head: torch.nn.Module = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> torch.nn.Embedding:
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value: torch.nn.Embedding) -> None:
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self) -> torch.nn.Module:
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings: torch.nn.Module) -> None:
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder: PlamoModel) -> None:
+        self.model = decoder
+
+    def get_decoder(self) -> PlamoModel:
+        return self.model
+
+    def forward(  # type: ignore
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+        assert input_ids is not None
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.Tensor,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: Any,
+    ) -> Dict[str, Any]:
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs: Dict[str, Any] = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values: List[torch.FloatTensor], beam_idx: int) -> Tuple[Any, ...]:
+        reordered_past: Tuple[Any, ...] = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past

pytorch_model-00001-of-00003.bin

@@ -0,0 +1,3 @@
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+size 9953812631

pytorch_model-00002-of-00003.bin

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+oid sha256:1da04d8f13b66bd506926b75ed95f2359c1b365c1147e776eb0bd0278e9938fe
+size 9896143321

pytorch_model-00003-of-00003.bin

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+size 6349271849

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<cls>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "sep_token": "<sep>",
+  "unk_token": "<unk>"
+}

tokenization_plamo.py

@@ -0,0 +1,159 @@
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+import transformers
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+logger = logging.get_logger(__name__)
+
+
+class PlamoTokenizer(PreTrainedTokenizer):  # type: ignore
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file: str,
+        unk_token: str = "<unk>",
+        bos_token: str = "<s>",
+        eos_token: str = "</s>",
+        pad_token: str = "<pad>",
+        cls_token: str = "<cls>",
+        sep_token: str = "<sep>",
+        mask_token: str = "<mask>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        clean_up_tokenization_spaces: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        if "add_bos_token" not in kwargs:
+            kwargs["add_bos_token"] = False
+        if "add_eos_token" not in kwargs:
+            kwargs["add_eos_token"] = False
+
+        super().__init__(
+            vocab_file=vocab_file,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            sep_token=sep_token,
+            mask_token=mask_token,
+            sp_model_kwargs=sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = kwargs["add_bos_token"]
+        self.add_eos_token = kwargs["add_eos_token"]
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+
+        self.add_tokens(self.all_special_tokens_extended, special_tokens=True)
+
+    # the functions below are copied from hf transformers LlamaTokenizer's implementation to fix the behaviour of the tokenizer
+    # https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/models/llama/tokenization_llama.py
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+        return state
+
+    def __setstate__(self, d: dict[str, Any]) -> None:
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+    @property
+    def vocab_size(self) -> Any:
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self) -> dict[str, int]:
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def convert_tokens_to_string(self, tokens: List[int]) -> str:
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens: List[int] = []
+        out_string = ""
+        prev_is_special = False
+        for i, token in enumerate(tokens):
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special and i != 0:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def _tokenize(self, text: str) -> Any:
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token: str) -> Any:
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index: int) -> Any:
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return ("",)
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+
+class PlamoConfig(transformers.LlamaConfig):  # type: ignore
+    model_type = "plamo"

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59fe756bef3dc5d4813bd2eb9aeb7c39138cbd71e665bc85e6a4c10e766465da
+size 1122464

tokenizer_config.json

@@ -0,0 +1,21 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_plamo.PlamoTokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<cls>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "<sep>",
+  "sp_model_kwargs": null,
+  "tokenizer_class": "PlamoTokenizer",
+  "unk_token": "<unk>"
+}